SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
SCONCE : a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing. / Hui, Sandra; Nielsen, Rasmus.
I: Bioinformatics, Bind 38, Nr. 7, 2022, s. 1801-1808.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - SCONCE
T2 - a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing
AU - Hui, Sandra
AU - Nielsen, Rasmus
N1 - Publisher Copyright: © 2022 The Author(s) 2022. Published by Oxford University Press.
PY - 2022
Y1 - 2022
N2 - Motivation: Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process. Results: We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. Availabilityand implementation: SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.
AB - Motivation: Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process. Results: We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. Availabilityand implementation: SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce.
U2 - 10.1093/bioinformatics/btac041
DO - 10.1093/bioinformatics/btac041
M3 - Journal article
C2 - 35080614
AN - SCOPUS:85128416157
VL - 38
SP - 1801
EP - 1808
JO - Bioinformatics (Online)
JF - Bioinformatics (Online)
SN - 1367-4811
IS - 7
ER -
ID: 341480018